Select n sequences from a collection#

Let’s load an alignment of primates to use in examples.

from cogent3 import get_app

loader = get_app("load_aligned", moltype="dna")
aln = loader("data/primate_brca1.fasta")
aln
0
ChimpanzeeTGTGGCACAAATACTCATGCCAGCTCATTACAGCATGAGAACAGTTTATTACTCACTAAA
Galago.......A................................G...................
HowlerMon...............................................G............
Rhesus...............................................G............
Orangutan............................................................
Gorilla............................................................
Human............................................................

7 x 2814 (truncated to 7 x 60) dna alignment

Select the first n sequences from an alignment#

Initialising take_n_seqs with the argument number=3 creates an app that returns the first 3 sequences from an alignment

Note

“first n” refers to the ordering in the fasta file.

from cogent3 import get_app

first_3 = get_app("take_n_seqs", number=3)
first_3(aln)
0
GalagoTGTGGCAAAAATACTCATGCCAGCTCATTACAGCATGAGAGCAGTTTATTACTCACTAAA
HowlerMon.......C................................A......G............
Rhesus.......C................................A......G............

3 x 2814 (truncated to 3 x 60) dna alignment

Randomly selecting n sequences from an alignment#

Using random=True and number=3 returns 3 random sequences. An optional argument for a seed can be provided to ensure the same sequences are returned each time the app is called.

from cogent3 import get_app

random_n = get_app("take_n_seqs", random=True, number=3, seed=1)
random_n(aln)
0
ChimpanzeeTGTGGCACAAATACTCATGCCAGCTCATTACAGCATGAGAACAGTTTATTACTCACTAAA
Rhesus...............................................G............
HowlerMon...............................................G............

3 x 2814 (truncated to 3 x 60) dna alignment

Selecting the same sequences from multiple alignments#

Providing the argument fixed_choice=True ensures the same sequences are returned when (randomly) sampling sequences across several alignments.

from cogent3 import get_app

loader = get_app("load_aligned", moltype="dna")
aln1 = loader("data/primate_brca1.fasta")
aln2 = loader("data/brca1.fasta")

aln1.names

['Galago',
 'HowlerMon',
 'Rhesus',
 'Orangutan',
 'Gorilla',
 'Human',
 'Chimpanzee']

aln2.names

['FlyingFox',
 'DogFaced',
 'FreeTaile',
 'LittleBro',
 'TombBat',
 'RoundEare',
 'FalseVamp',
 'LeafNose',
 'Horse',
 'Rhino',
 'Pangolin',
 'Cat',
 'Dog',
 'Llama',
 'Pig',
 'Cow',
 'Hippo',
 'SpermWhale',
 'HumpbackW',
 'Mole',
 'Hedgehog',
 'TreeShrew',
 'FlyingLem',
 'Galago',
 'HowlerMon',
 'Rhesus',
 'Orangutan',
 'Gorilla',
 'Human',
 'Chimpanzee',
 'Jackrabbit',
 'FlyingSqu',
 'OldWorld',
 'Mouse',
 'Rat',
 'NineBande',
 'HairyArma',
 'Anteater',
 'Sloth',
 'Dugong',
 'Manatee',
 'AfricanEl',
 'AsianElep',
 'RockHyrax',
 'TreeHyrax',
 'Aardvark',
 'GoldenMol',
 'Madagascar',
 'Tenrec',
 'LesserEle',
 'GiantElep',
 'Caenolest',
 'Phascogale',
 'Wombat',
 'Bandicoot']

fixed_choice = get_app("take_n_seqs", number=2, random=True, fixed_choice=True)
result1 = fixed_choice(aln1).names
result2 = fixed_choice(aln2).names
result1 == result2

True